Being on a data team, you’re well aware of the probability of data quality issues lurking in your tables—and the importance of testing for them.
But is your boss?
If managers and executives don’t fully appreciate the stakes of a problem, then selling them on a project or tool to address that problem can be pulling teeth.
That’s why decision-makers’ buy-in for a project and all the related technology is arguably even more important than the project itself. If you can’t secure the former, then forget about the latter.
This means it’s on you to come up with the most effective possible pitch for your projects. These pitches should be concise yet persuasive, and articulate the urgency of your work without being too verbose or getting bogged down in jargon.
Easier said than done, to be sure—which is why we’ve created the following guidance and sample message you can use to create your pitches, secure your buy-in, and finally get started on the data quality work you need to do.
Know your audience
Not all stakeholders have the same concerns. The same message can resonate or fall flat depending on who you’re talking to. Lucky for you, bad data can threaten your business in myriad ways (we never thought we’d describe that as ‘lucky’), which lets you tailor your message to whomever your audience may be.
Talking to your team manager? Focus on the ways in which low-quality data can undermine the processes you rely on to do your work. CFO? Talk about the financial impacts of data quality issues. CEO or head of PR? They might respond more to the threat of reputational damage or customer confidence that bad data imposes.
Whoever has the power of the purse, it’s likely that bad data can rear its head in an area they care about. It’s your job to figure out what that area is, how your proposed data quality tests will offer protection or benefits in that area, and draw a clear line between the two.
Be the right amount of technical
It’s not enough to tailor your message to the issues your audience cares about. It’s also critical to align it to their level of technical comfort.
If your audience is something of a layperson when it comes to data quality, consider speaking in terms of data quality dimensions—concepts like accuracy and completeness and the stakes around them—instead of the technical specifics of a particular test.
If you want to report on previous data quality tests you’ve performed to demonstrate what they can accomplish and why you should move forward with a new project, consider aggregating your results for a nontechnical audience instead of walking through them test by test.
Sample message
Below is a template for an email you can send to a decision-maker to gain buy-in for your data quality project.
This example assumes the audience is the manager of a technical team, and that the message can therefore get into the technical weeds a bit. Next, we’ll discuss how to tailor the template to a less technical audience.
Hi [name],
As you know, we’ve recently encountered [description of issues]. These have caused [negative outcomes in an area important to your audience]. Based on these occurrences, it’s also logical to expect [one to two other potential problems].
The good news is that we can solve this issue, and prevent further issues, with a robust data quality process. I’m going to start investigating procedures and tools that can help us with this. I’ll keep you in the loop on matters like pricing and timeline as I proceed. Thanks!
If you’re sending your message to an executive or VP several degrees removed from a technical team, you can use roughly the same message structure. But for this audience, assume less familiarity with on-the-ground data issues: err on the side of more context and less (or no) technical detail.
It’s also a good idea to include some kind of social proof from an authoritative source that your recipient cares about—even if it's not a source you would use yourself. For example, C-level executives might respond positively to a Gartner article that emphasizes the ways data quality affects their areas of interest.
Note that, while the two approaches have their differences, both are structured to solicit an active ‘no’ if the decision-maker disapproves of the project, and to imply that a nonresponse means ‘yes.’
Take the first step
Once you've gotten your boss in the loop, you can start out on your data quality journey! Here's our advice on where to start with data quality.
Be sure to check out GX Cloud to see if it's the right platform for your data quality process.
